@article{athey2017efficient,
  title={Efficient policy learning},
  author={Athey, Susan and Wager, Stefan},
  journal={arXiv preprint arXiv:1702.02896},
  year={2017}
}

@article{kitagawa2018should,
  title={Who should be treated? empirical welfare maximization methods for treatment choice},
  author={Kitagawa, Toru and Tetenov, Aleksey},
  journal={Econometrica},
  volume={86},
  number={2},
  pages={591--616},
  year={2018},
  publisher={Wiley Online Library},
  doi = {10.3982/ECTA13288},
  url = {https://doi.org/10.3982/ECTA13288}
}

@article{zhou2018offline,
  title={Offline multi-action policy learning: Generalization and optimization},
  author={Zhou, Zhengyuan and Athey, Susan and Wager, Stefan},
  journal={arXiv preprint arXiv:1810.04778},
  year={2018}
}

@article{mbakop2016model,
  title={Model selection for treatment choice: Penalized welfare maximization},
  author={Mbakop, Eric and Tabord-Meehan, Max},
  journal={arXiv preprint arXiv:1609.03167},
  year={2016}
}

@article{athey2019generalized,
  title={Generalized random forests},
  author={Athey, Susan and Tibshirani, Julie and Wager, Stefan},
  journal={The Annals of Statistics},
  volume={47},
  number={2},
  pages={1148--1178},
  year={2019},
  publisher={Institute of Mathematical Statistics}
}

@article{bloom1997benefits,
  title={The benefits and costs of JTPA Title II-A programs: Key findings from the National Job Training Partnership Act study},
  author={Bloom, Howard S and Orr, Larry L and Bell, Stephen H and Cave, George and Doolittle, Fred and Lin, Winston and Bos, Johannes M and others},
  journal={Journal of human resources},
  volume={32},
  number={3},
  year={1997},
  publisher={University of Wisconsin Press},
  doi = {10.2307/146183},
  url = {https://doi.org/10.2307/146183}
}

@inproceedings{kallus2018confounding,
  title={Confounding-robust policy improvement},
  author={Kallus, Nathan and Zhou, Angela},
  booktitle={Advances in Neural Information Processing Systems},
  pages={9269--9279},
  year={2018}
}

@article{eddelbuettel2011rcpp,
  title={Rcpp: Seamless R and C++ integration},
  author={Eddelbuettel, Dirk and Fran{\c{c}}ois, Romain and Allaire, J and Ushey, Kevin and Kou, Qiang and Russel, N and Chambers, John and Bates, D},
  journal={Journal of Statistical Software},
  volume={40},
  number={8},
  pages={1--18},
  year={2011}
}

@article{JSSv061i01,
   author = {Thomas Grubinger and Achim Zeileis and Karl-Peter Pfeiffer},
   title = {evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R},
   journal = {Journal of Statistical Software},
   volume = {61},
   number = {1},
   year = {2014},
   keywords = {},
   abstract = {Commonly used classification and regression tree methods like the CART algorithm are recursive partitioning methods that build the model in a forward stepwise search. Although this approach is known to be an efficient heuristic, the results of recursive tree methods are only locally optimal, as splits are chosen to maximize homogeneity at the next step only. An alternative way to search over the parameter space of trees is to use global optimization methods like evolutionary algorithms. This paper describes the evtree package, which implements an evolutionary algorithm for learning globally optimal classification and regression trees in R. Computationally intensive tasks are fully computed in C++ while the partykit package is leveraged for representing the resulting trees in R, providing unified infrastructure for summaries, visualizations, and predictions. evtree is compared to the open-source CART implementation rpart, conditional inference trees (ctree), and the open-source C4.5 implementation J48. A benchmark study of predictive accuracy and complexity is carried out in which evtree achieved at least similar and most of the time better results compared to rpart, ctree, and J48. Furthermore, the usefulness of evtree in practice is illustrated in a textbook customer classification task.},
   issn = {1548-7660},
   pages = {1--29},
   doi = {10.18637/jss.v061.i01},
   url = {https://www.jstatsoft.org/v061/i01}
}

@article{manski2004statistical,
  title={Statistical treatment rules for heterogeneous populations},
  author={Manski, Charles F},
  journal={Econometrica},
  volume={72},
  number={4},
  pages={1221--1246},
  year={2004},
  publisher={Wiley Online Library},
  doi = {10.1111/j.1468-0262.2004.00530.x},
  url = {https://www.jstor.org/stable/3598783}
}

@article{zhao2012estimating,
  title={Estimating individualized treatment rules using outcome weighted learning},
  author={Zhao, Yingqi and Zeng, Donglin and Rush, A John and Kosorok, Michael R},
  journal={Journal of the American Statistical Association},
  volume={107},
  number={499},
  pages={1106--1118},
  year={2012},
  publisher={Taylor \& Francis}
}

@article{swaminathan2015batch,
  title={Batch learning from logged bandit feedback through counterfactual risk minimization},
  author={Swaminathan, Adith and Joachims, Thorsten},
  journal={The Journal of Machine Learning Research},
  volume={16},
  number={1},
  pages={1731--1755},
  year={2015},
  publisher={JMLR. org}
}

@manual{R,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2020},
  url = {https://www.R-project.org/}
}

@article{luedtke2016super,
  title={Super-learning of an optimal dynamic treatment rule},
  author={Luedtke, Alexander R and {van der Laan}, Mark},
  journal={The international journal of biostatistics},
  volume={12},
  number={1},
  pages={305--332},
  year={2016},
  publisher={De Gruyter},
  doi = {10.1515/ijb-2015-0052},
  url = {https://doi.org/10.1515/ijb-2015-0052}
}

@article{van2015targeted,
  title={Targeted learning of the mean outcome under an optimal dynamic treatment rule},
  author={{van der Laan}, Mark and Luedtke, Alexander R},
  journal={Journal of causal inference},
  volume={3},
  number={1},
  pages={61--95},
  year={2015},
  publisher={De Gruyter}
}

@manual{tmle3,
  title = {tmle3mopttx: Targeted Maximum Likelihood Estimation of the Mean under Optimal Individualized Treatment},
  author = {Ivana Malenica and Jeremy Coyle and Mark {van der Laan}},
  year = {2020},
  note = {R package version 0.1.0},
  url = {https://tlverse.org/tmle3mopttx},
}
